Suvansh Sanjeev
Papers
1
Total Citations
4
H-Index
1
About
Suvansh Sanjeev is a researcher focused on the intersection of formal methods, control theory, and machine learning for safe autonomous systems. His primary contributions lie in developing scalable frameworks for Hamilton-Jacobi (HJ) reachability analysis, a powerful tool for guaranteeing safety in dynamic environments. In his most cited work, “Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability” (2021), Sanjeev addresses a critical bottleneck: the computational intractability of HJ methods for high-dimensional systems. By integrating learning-based techniques, he enables the efficient computation of provably safe sets and controllers, even when environmental dynamics are uncertain or partially unknown. This work has direct implications for safety-critical applications such as aircraft collision avoidance and assistive robotics. With 4 citations, his research is gaining traction in the formal verification and robotics communities. Sanjeev’s contributions are notable for bridging the gap between rigorous theoretical guarantees and practical, scalable deployment, making him a rising voice in the quest for trustworthy autonomy.
Research Focus
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Top Papers
- 1